Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network

Yingying Ma; Wei Lan; Chenlei Leng; Ting Li; Hansheng Wang

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arXiv · 2412.18145

Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network

Yingying Ma·Wei Lan·Chenlei Leng·Ting Li·Hansheng Wang

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Abstract

The social characteristics of players in a social network are closely associated with their network positions and relational importance. Identifying those influential players in a network is of great importance as it helps to understand how ties are formed, how information is propagated, and, in turn, can guide the dissemination of new information. Motivated by a Sina Weibo social network analysis of the 2021 Henan Floods, where response variables for each Sina Weibo user are available, we propose a new notion of supervised centrality that emphasizes the task-specific nature of a player's centrality. To estimate the supervised centrality and identify important players, we develop a novel sparse network influence regression by introducing individual heterogeneity for each user. To overcome the computational difficulties in fitting the model for large social networks, we further develop a forward-addition algorithm and show that it can consistently identify a superset of the influential Sina Weibo users. We apply our method to analyze three responses in the Henan Floods data: the number of comments, reposts, and likes, and obtain meaningful results. A further simulation study corroborates the developed method.

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Yingying Ma, Wei Lan, Chenlei Leng, Ting Li, Hansheng Wang. 2024-12-24. Supervised centrality via sparse network influence regression: an application to the 2021 Henan floods' social network. https://arxiv.org/abs/2412.18145

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